PhysCodeGym: Benchmarking Coding Agents for Physically Verifiable 4D Reconstruction
We introduce PhysCodeGym, a benchmark for physically verifiable 4D reconstruction with $156$ tasks covering rigid-body, liquid, deformable, and wind-driven motion.
ProofPaper ↗
Key points
- Building physical simulations from video requires coding agents to translate observations into executable models and refine them through tool use.
- Six metrics combine rule-based measurements with rubric-based judgments to assess visual and physical reconstruction quality.
- Program inspection, scene measurements, and counterfactual interventions test physical consistency under original and modified conditions.
- Experiments with frontier coding agents reveal a tradeoff between artifact quality and completion across reasoning-effort settings, while harness comparisons and agent traces link verification behavior with reconstruction performance.
Sources (1)
- [1]PhysCodeGym: Benchmarking Coding Agents for Physically Verifiable 4D ReconstructionarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 12, 06:44 PM
We introduce PhysCodeGym, a benchmark for physically verifiable 4D reconstruction with $156$ tasks covering rigid-body, liquid, deformable, and wind-driven motion.
Building physical simulations from video requires coding agents to translate observations into executable models and refine them through tool use.
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